Goals for DGE Analysis

The goals for this analysis are to investigate how the gene expression changes based on the mouse model, genotype within the mouse model, and age. Questions to answer: -What genes are deferentially expressed due to Mouse Model Alone? -How does aging affect gene expression within each mouse model? -How does the genotype within each mouse model affect gene expression at different time points?

Import experimental design matrix.

library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

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    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max,
    which.min


Attaching package: ‘S4Vectors’

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    expand.grid, I, unname

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
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Loading required package: matrixStats

Attaching package: ‘MatrixGenerics’

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    colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds,
    colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs,
    colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds,
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    rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs,
    rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats,
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    rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds,
    rowWeightedVars

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
    see 'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: ‘Biobase’

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Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. 
library(pheatmap)
library(dplyr)

Attaching package: ‘dplyr’

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library(dendextend)

---------------------
Welcome to dendextend version 1.15.2
Type citation('dendextend') for how to cite the package.

Type browseVignettes(package = 'dendextend') for the package vignette.
The github page is: https://github.com/talgalili/dendextend/

Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
You may ask questions at stackoverflow, use the r and dendextend tags: 
     https://stackoverflow.com/questions/tagged/dendextend

    To suppress this message use:  suppressPackageStartupMessages(library(dendextend))
---------------------


Attaching package: ‘dendextend’

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design_matrix<-read.table('/Users/tasnimtabassum/Documents/Transcriptomics_SP22/Experimental_Design_TG.csv',sep=',',header=TRUE)

head(design_matrix)
rownames(design_matrix)<-design_matrix$Sample
design_matrix$Sample<-NULL
design_matrix

Import matrix containing the counts for all samples. These counts represent the forward strand counts since this was a stranded library.

counts_matrix<-read.table("/Users/tasnimtabassum/Documents/Transcriptomics_SP22/GitHub/Transcriptomics-Final-Project-/Count_Tables/allcounts.csv",sep=',',header=TRUE)
counts_matrix

Because the numbers after the dot in the ensembl IDs represent versions of genes in certain annotations, we can remove these to more easily conduct our differential gene expression analysis.

counts_matrix$V1<-gsub("\\..*","",counts_matrix$V1)
counts_matrix
# remove the "V1" from col 1
rownames(counts_matrix)<-counts_matrix$V1
counts_matrix$V1<-NULL

head(counts_matrix)

We need to sort the counts_matrix as well as our experimental design matrix in order to run DESEQ2

counts_matrix<-counts_matrix[,order(colnames(counts_matrix))]
counts_matrix
design_matrix<-design_matrix[order(rownames(design_matrix)),]
design_matrix
dds <- DESeqDataSetFromMatrix(countData = counts_matrix,
                              colData = design_matrix,
                              design = ~ Genotype + Age+ Model)
some variables in design formula are characters, converting to factors  the design formula contains one or more numeric variables with integer values,
  specifying a model with increasing fold change for higher values.
  did you mean for this to be a factor? if so, first convert
  this variable to a factor using the factor() function
  the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  Including numeric variables with large mean can induce collinearity with the intercept.
  Users should center and scale numeric variables in the design to improve GLM convergence.
dds
class: DESeqDataSet 
dim: 46075 72 
metadata(1): version
assays(1): counts
rownames(46075): ENSMUSG00000000001 ENSMUSG00000000003 ... N_noFeature N_unmapped
rowData names(0):
colnames(72): SRR8512301 SRR8512302 ... SRR8512439 SRR8512440
colData names(3): Model Genotype Age

Remove genes with counts less than 10

keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]

Run DESeq

dds <- DESeq(dds)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 5166 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
rawcounts.matrix <- counts(dds,normalized=F)
normalizedcounts.matrix <- counts(dds,normalized=T)
vst_dds <- vst(dds)
dists <- dist(t(assay(vst_dds)))
dists
           SRR8512301 SRR8512302 SRR8512303 SRR8512304 SRR8512307 SRR8512308 SRR8512309 SRR8512310
SRR8512302   34.98989                                                                             
SRR8512303   24.75489   33.13232                                                                  
SRR8512304   29.32833   34.57388   24.73419                                                       
SRR8512307   21.09034   36.71252   24.89597   30.84387                                            
SRR8512308   31.23042   19.83868   32.57351   35.74883   31.15181                                 
SRR8512309   25.47078   41.31314   22.72178   29.07433   22.56086   37.35232                      
SRR8512310   23.98892   29.27166   21.56907   25.83143   22.57367   29.23152   25.59670           
SRR8512313   22.22640   34.21704   22.53681   25.42066   21.83970   32.57706   20.81997   16.75686
SRR8512318   24.51173   34.21059   21.70783   30.77728   26.62417   33.64663   28.42689   22.88748
SRR8512319   37.11827   20.57906   34.48485   35.85855   37.39977   20.50508   40.79025   32.15999
SRR8512324   18.20493   35.47741   24.93245   30.50401   19.92692   29.94846   24.55149   24.51515
SRR8512325   31.55144   46.28219   37.96788   40.33317   34.98445   44.23550   36.88122   35.36607
SRR8512326   22.23004   32.71514   26.44609   28.07123   25.84814   30.21772   28.13695   24.39279
SRR8512329   21.08136   33.40568   19.13528   26.81156   20.09536   30.27128   18.33309   20.77168
           SRR8512313 SRR8512318 SRR8512319 SRR8512324 SRR8512325 SRR8512326 SRR8512329 SRR8512330
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318   25.25760                                                                             
SRR8512319   35.60471   38.89835                                                                  
SRR8512324   22.96872   27.10411   35.95050                                                       
SRR8512325   32.69575   39.19733   47.68236   30.97220                                            
SRR8512326   23.49090   30.52538   33.49744   21.40496   33.68735                                 
SRR8512329   20.09278   21.85412   33.85347   21.36197   35.52867   24.62444                      
           SRR8512331 SRR8512332 SRR8512335 SRR8512336 SRR8512341 SRR8512342 SRR8512347 SRR8512348
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318                                                                                        
SRR8512319                                                                                        
SRR8512324                                                                                        
SRR8512325                                                                                        
SRR8512326                                                                                        
SRR8512329                                                                                        
           SRR8512349 SRR8512350 SRR8512351 SRR8512352 SRR8512353 SRR8512354 SRR8512363 SRR8512364
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318                                                                                        
SRR8512319                                                                                        
SRR8512324                                                                                        
SRR8512325                                                                                        
SRR8512326                                                                                        
SRR8512329                                                                                        
           SRR8512365 SRR8512366 SRR8512371 SRR8512372 SRR8512373 SRR8512374 SRR8512375 SRR8512376
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318                                                                                        
SRR8512319                                                                                        
SRR8512324                                                                                        
SRR8512325                                                                                        
SRR8512326                                                                                        
SRR8512329                                                                                        
           SRR8512379 SRR8512380 SRR8512381 SRR8512382 SRR8512384 SRR8512385 SRR8512386 SRR8512387
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318                                                                                        
SRR8512319                                                                                        
SRR8512324                                                                                        
SRR8512325                                                                                        
SRR8512326                                                                                        
SRR8512329                                                                                        
           SRR8512389 SRR8512390 SRR8512399 SRR8512400 SRR8512401 SRR8512402 SRR8512405 SRR8512406
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318                                                                                        
SRR8512319                                                                                        
SRR8512324                                                                                        
SRR8512325                                                                                        
SRR8512326                                                                                        
SRR8512329                                                                                        
           SRR8512407 SRR8512408 SRR8512411 SRR8512412 SRR8512426 SRR8512427 SRR8512428 SRR8512429
SRR8512302                                                                                        
SRR8512303                                                                                        
SRR8512304                                                                                        
SRR8512307                                                                                        
SRR8512308                                                                                        
SRR8512309                                                                                        
SRR8512310                                                                                        
SRR8512313                                                                                        
SRR8512318                                                                                        
SRR8512319                                                                                        
SRR8512324                                                                                        
SRR8512325                                                                                        
SRR8512326                                                                                        
SRR8512329                                                                                        
           SRR8512430 SRR8512431 SRR8512432 SRR8512433 SRR8512434 SRR8512435 SRR8512439
SRR8512302                                                                             
SRR8512303                                                                             
SRR8512304                                                                             
SRR8512307                                                                             
SRR8512308                                                                             
SRR8512309                                                                             
SRR8512310                                                                             
SRR8512313                                                                             
SRR8512318                                                                             
SRR8512319                                                                             
SRR8512324                                                                             
SRR8512325                                                                             
SRR8512326                                                                             
SRR8512329                                                                             
 [ reached getOption("max.print") -- omitted 57 rows ]

TO DO: *****Label samples


dendogram<-plot(hclust(dists),cex=0.6)

labels(dendogram)
character(0)

Trying to rename labels….

dend <- dists %>%  hclust %>% as.dendrogram
dend %>% set("labels", c(rep('rtg4510',26),rep('J20',42),rep('rtg4510',4))) %>% set("labels_cex", 0.5) %>% plot

`

*** Add title, make graph cute (size bigger) Capitalize group

plotPCA(vst_dds,intgroup=c("Model","Genotype","Age"))

Obtain results based on different contrasts

What genes are differentially expressed in tau pathology? mutant vs wt

res_1 <- results(dds, contrast = c("Genotype","rtg4510","WT_TG"))
res1_ordered <- res_1[order(res_1$pvalue),] 
head(res1_ordered,200)
log2 fold change (MLE): Genotype rtg4510 vs WT_TG 
Wald test p-value: Genotype rtg4510 vs WT_TG 
DataFrame with 200 rows and 6 columns
                    baseMean log2FoldChange     lfcSE      stat      pvalue        padj
                   <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
ENSMUSG00000079037 14497.968       1.262646 0.0659797   19.1369 1.24473e-81 2.38740e-77
ENSMUSG00000021171   692.285      -1.037739 0.0642636  -16.1482 1.17004e-58 1.12207e-54
ENSMUSG00000056553  4190.567      -1.151315 0.0796032  -14.4632 2.07048e-47 1.32373e-43
ENSMUSG00000018411  7052.860       0.863156 0.0677374   12.7427 3.42466e-37 1.64213e-33
ENSMUSG00000000805   334.382       1.055046 0.0836956   12.6057 1.96305e-36 7.53026e-33
...                      ...            ...       ...       ...         ...         ...
ENSMUSG00000021886   4.84672       3.074444 0.5178245   5.93723 2.89874e-09 2.85117e-07
ENSMUSG00000021032 107.68873      -0.889651 0.1499263  -5.93392 2.95783e-09 2.87998e-07
ENSMUSG00000060176  36.76433      -0.755378 0.1272986  -5.93391 2.95807e-09 2.87998e-07
ENSMUSG00000022358 192.95473       0.589465 0.0998653   5.90260 3.57808e-09 3.46604e-07
ENSMUSG00000037946 104.42285       0.389471 0.0660163   5.89962 3.64344e-09 3.51162e-07

Filter the res1_ordered byp value to obtain only those genes with p value less than 0.05

#Filter Differentially Expressed Genes by p value 0.05
res1_ordered_filtered <- res1_ordered[res1_ordered$pvalue<0.05,]

See how many genes have p value equals to zero

res1_ordered_filtered_2 <- res1_ordered[res1_ordered$pvalue==0,]

Genes with pvalue==0

res1_ordered_filtered_2
log2 fold change (MLE): Genotype rtg4510 vs WT_TG 
Wald test p-value: Genotype rtg4510 vs WT_TG 
DataFrame with 0 rows and 6 columns

Genes with pvalue<0.05

res1_ordered_filtered
log2 fold change (MLE): Genotype rtg4510 vs WT_TG 
Wald test p-value: Genotype rtg4510 vs WT_TG 
DataFrame with 5604 rows and 6 columns
                    baseMean log2FoldChange     lfcSE      stat      pvalue        padj
                   <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
ENSMUSG00000079037 14497.968       1.262646 0.0659797   19.1369 1.24473e-81 2.38740e-77
ENSMUSG00000021171   692.285      -1.037739 0.0642636  -16.1482 1.17004e-58 1.12207e-54
ENSMUSG00000056553  4190.567      -1.151315 0.0796032  -14.4632 2.07048e-47 1.32373e-43
ENSMUSG00000018411  7052.860       0.863156 0.0677374   12.7427 3.42466e-37 1.64213e-33
ENSMUSG00000000805   334.382       1.055046 0.0836956   12.6057 1.96305e-36 7.53026e-33
...                      ...            ...       ...       ...         ...         ...
ENSMUSG00000036208   80.7554      0.1211650 0.0617908   1.96089   0.0498920    0.176325
ENSMUSG00000052926  336.0837     -0.1012596 0.0516416  -1.96081   0.0499006    0.176325
ENSMUSG00000047242  413.5181      0.0903139 0.0460635   1.96064   0.0499211    0.176365
ENSMUSG00000024862 2200.1750     -0.1517860 0.0774291  -1.96032   0.0499580    0.176463
ENSMUSG00000029802  305.8101      0.1549010 0.0790213   1.96024   0.0499673    0.176463

-How does aging affect gene expression within each mouse model?

res_2 <- results(dds, contrast = c("Age","2","8"))
Error in cleanContrast(object, contrast, expanded = isExpanded, listValues = listValues,  : 
  Age is not a factor, see ?results
?results

-How does the genotype within each mouse model affect gene expression at different time points?

#GO-Term Enrichment

Install Mouse annotation library:

BiocManager::install("org.Mm.eg.db")
'getOption("repos")' replaces Bioconductor standard repositories, see '?repositories' for details

replacement repositories:
    CRAN: https://cran.rstudio.com/

Bioconductor version 3.15 (BiocManager 1.30.17), R 4.2.0 (2022-04-22)
package(s) not installed when version(s) same as current; use `force = TRUE` to re-install: 'org.Mm.eg.db'
install.packages("devtools")
Error in install.packages : Updating loaded packages
devtools::install_github("stephenturner/annotables")
Downloading GitHub repo stephenturner/annotables@HEAD
  
   checking for file ‘/private/var/folders/6y/jp6148xn7bj2wzbf3h_c4xt00000gn/T/RtmpNSybFS/remotes14c43a534409/stephenturner-annotables-631423c/DESCRIPTION’ ...
  
✔  checking for file ‘/private/var/folders/6y/jp6148xn7bj2wzbf3h_c4xt00000gn/T/RtmpNSybFS/remotes14c43a534409/stephenturner-annotables-631423c/DESCRIPTION’

  
─  preparing ‘annotables’:

  
   checking DESCRIPTION meta-information ...
  
✔  checking DESCRIPTION meta-information

  
─  checking for LF line-endings in source and make files and shell scripts

  
─  checking for empty or unneeded directories

  
     NB: this package now depends on R (>= 3.5.0)

  
     WARNING: Added dependency on R >= 3.5.0 because serialized objects in
     serialize/load version 3 cannot be read in older versions of R.
     File(s) containing such objects:
       ‘annotables/data/bdgp6.rda’ ‘annotables/data/bdgp6_tx2gene.rda’
       ‘annotables/data/ensembl_version.rda’ ‘annotables/data/galgal5.rda’
       ‘annotables/data/galgal5_tx2gene.rda’ ‘annotables/data/grch37.rda’
       ‘annotables/data/grch37_tx2gene.rda’ ‘annotables/data/grch38.rda’
       ‘annotables/data/grch38_tx2gene.rda’ ‘annotables/data/grcm38.rda’
       ‘annotables/data/grcm38_tx2gene.rda’ ‘annotables/data/mmul801.rda’
       ‘annotables/data/mmul801_tx2gene.rda’ ‘annotables/data/rnor6.rda’
       ‘annotables/data/rnor6_tx2gene.rda’ ‘annotables/data/wbcel235.rda’
       ‘annotables/data/wbcel235_tx2gene.rda’
─  building ‘annotables_0.1.91.tar.gz’

  
   
* installing *source* package ‘annotables’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (annotables)
install.packages("ggnewscale")
trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/ggnewscale_0.4.7.tgz'
Content type 'application/x-gzip' length 342456 bytes (334 KB)
==================================================
downloaded 334 KB

The downloaded binary packages are in
    /var/folders/6y/jp6148xn7bj2wzbf3h_c4xt00000gn/T//RtmpNSybFS/downloaded_packages
library(biomaRt) #For conversion of transcript IDs to gene ID
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
library(annotables) #to retrieve grcm38 annotation for mouse genome
library(org.Mm.eg.db) #Mouse genome annotation
Loading required package: AnnotationDbi

Attaching package: ‘AnnotationDbi’

The following object is masked from ‘package:dplyr’:

    select
library(DOSE)
DOSE v3.22.0  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
library(pathview)

##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.

The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################
library(clusterProfiler)
clusterProfiler v4.4.1  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141

Attaching package: ‘clusterProfiler’

The following object is masked from ‘package:AnnotationDbi’:

    select

The following object is masked from ‘package:biomaRt’:

    select

The following object is masked from ‘package:IRanges’:

    slice

The following object is masked from ‘package:S4Vectors’:

    rename

The following object is masked from ‘package:stats’:

    filter
library(AnnotationHub) 
Loading required package: BiocFileCache
Loading required package: dbplyr

Attaching package: ‘dbplyr’

The following objects are masked from ‘package:dplyr’:

    ident, sql

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio

Attaching package: ‘AnnotationHub’

The following object is masked from ‘package:Biobase’:

    cache
library(ensembldb)
Loading required package: GenomicFeatures
Loading required package: AnnotationFilter

Attaching package: 'ensembldb'

The following object is masked from 'package:clusterProfiler':

    filter

The following object is masked from 'package:dplyr':

    filter

The following object is masked from 'package:stats':

    filter
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ stringr 1.4.0
✔ tidyr   1.2.0     ✔ forcats 0.5.1
✔ readr   2.1.2     
── Conflicts ─────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::collapse()         masks IRanges::collapse()
✖ dplyr::combine()          masks Biobase::combine(), BiocGenerics::combine()
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library(ggnewscale)
grcm38
res1_ordered
log2 fold change (MLE): Genotype rtg4510 vs WT_TG 
Wald test p-value: Genotype rtg4510 vs WT_TG 
DataFrame with 25644 rows and 6 columns
                    baseMean log2FoldChange     lfcSE      stat      pvalue        padj
                   <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
ENSMUSG00000079037 14497.968       1.262646 0.0659797   19.1369 1.24473e-81 2.38740e-77
ENSMUSG00000021171   692.285      -1.037739 0.0642636  -16.1482 1.17004e-58 1.12207e-54
ENSMUSG00000056553  4190.567      -1.151315 0.0796032  -14.4632 2.07048e-47 1.32373e-43
ENSMUSG00000018411  7052.860       0.863156 0.0677374   12.7427 3.42466e-37 1.64213e-33
ENSMUSG00000000805   334.382       1.055046 0.0836956   12.6057 1.96305e-36 7.53026e-33
...                      ...            ...       ...       ...         ...         ...
ENSMUSG00000100419 13.060758              0  0.764635         0           1           1
ENSMUSG00000101153  0.357632              0  1.197851         0           1          NA
ENSMUSG00000103043  0.168415              0  2.436069         0           1          NA
ENSMUSG00000103706  0.655486              0  1.022463         0           1          NA
ENSMUSG00000104658  0.120422              0  2.521910         0           1          NA
idx <- grcm38$ensgene %in% rownames(res1_ordered)

head(idx)
[1]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
non_duplicates <- which(duplicated(ids$ensgene) == FALSE)
ids <- ids[non_duplicates, ]
res_tableOE_tb <- res1_ordered_filtered %>%
  data.frame() %>%
  rownames_to_column(var="gene") %>% 
  as_tibble()
library("dplyr")
library("annotables")
res_ids = inner_join(res_tableOE_tb, ids, by=c("gene"="ensgene"))
res_ids
sigOE_genes <- as.character(sigOE$ensgene)
Unknown or uninitialised column: `ensgene`.
## Run GO enrichment analysis 
ego <- enrichGO(gene = sigOE_genes, 
                universe =ids$ensgene ,
                keyType = "ENSEMBL",
                OrgDb = org.Mm.eg.db, 
                ont = "BP", 
                pAdjustMethod = "BH", 
                qvalueCutoff = 0.05, 
                readable = TRUE,
                pool=TRUE)
                
## Output results from GO analysis to a table
cluster_summary <- data.frame(ego)
dotplot(ego, showCategory=50)


xyz = pairwise_termsim(ego)
Error in pairwise_termsim(ego) : 
  could not find function "pairwise_termsim"

Because we have transcript IDs, we want ensembl ids in order to be able to conduct GO Term enrichment we need ensembl ID

In order to conduct the hypergeometric test on each set of differentially expressed genes we need to have two sets of genes: a background set, and a significant differentially expressed gene set. The background set will be comprised of all differentially expressed genes and the genes of interest will be those with significant p values (0.05).

We are using ‘ALL’ for ontology because we want to see all of the differentially expressed genes accross all categories. We are using the bonferroni correction.

## Use mouse genome 
ego <- enrichGO(gene = rownames(res1_ordered_filtered), 
                universe =ids$ensgene ,
                keyType = "ENSEMBL",
                OrgDb = org.Mm.eg.db, 
                ont = "ALL", 
                pAdjustMethod = "BH", 
                qvalueCutoff = 0.05, 
                readable = TRUE,
                pool=TRUE)
                
## Output results from GO analysis to a table
enriched_genes_res1 <- data.frame(ego)
dotplot(ego, showCategory=50)


OE_foldchanges <- res1_ordered_filtered$log2FoldChange
OE_foldchanges<-head(OE_foldchanges,150)

names(OE_foldchanges) <- rownames(head(res1_ordered_filtered,150))

cnetplot(ego, 
         categorySize="pvalue", 
         showCategory = 5, 
         foldChange=OE_foldchanges, 
         vertex.label.font=2)
Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

emapplot(ego, showCategory = 50)
Error in has_pairsim(x) : 
  Term similarity matrix not available. Please use pairwise_termsim function to deal with the results of enrichment analysis.
---
title: "DESeq2 Multifactor Analysis Final Project"
output: html_notebook
---

# Goals for DGE Analysis

The goals for this analysis are to investigate how the gene expression changes based on the mouse model, genotype within the mouse model, and age.
Questions to answer:
-What genes are deferentially expressed due to Mouse Model Alone?
-How does aging affect gene expression within each mouse model?
-How does the genotype within each mouse model affect gene expression at different time points?

### Import experimental design matrix.

```{r}
library(DESeq2)
library(pheatmap)
library(dplyr)
library(dendextend)
```


```{r}
design_matrix<-read.table('/Users/tasnimtabassum/Documents/Transcriptomics_SP22/Experimental_Design_TG.csv',sep=',',header=TRUE)

head(design_matrix)
```


```{r}
rownames(design_matrix)<-design_matrix$Sample
design_matrix$Sample<-NULL
```
```{r}
design_matrix
```
Import matrix containing the counts for all samples. These counts represent the forward strand counts since this was a stranded library. 

```{r}
counts_matrix<-read.table("/Users/tasnimtabassum/Documents/Transcriptomics_SP22/GitHub/Transcriptomics-Final-Project-/Count_Tables/allcounts.csv",sep=',',header=TRUE)
```
```{r}
counts_matrix
```
Because the numbers after the dot in the ensembl IDs represent versions of genes in certain annotations, we can remove these to more easily conduct our differential gene expression analysis. 
```{r}
counts_matrix$V1<-gsub("\\..*","",counts_matrix$V1)
```
```{r}
counts_matrix
```

```{r}
# remove the "V1" from col 1
rownames(counts_matrix)<-counts_matrix$V1
counts_matrix$V1<-NULL

head(counts_matrix)
```

We need to sort the counts_matrix as well as our experimental design matrix in order to run DESEQ2    
```{r}
counts_matrix<-counts_matrix[,order(colnames(counts_matrix))]
```
```{r}
counts_matrix
```
```{r}
design_matrix<-design_matrix[order(rownames(design_matrix)),]
```
```{r}
design_matrix
```


```{r}
dds <- DESeqDataSetFromMatrix(countData = counts_matrix,
                              colData = design_matrix,
                              design = ~ Genotype + Age+ Model)
dds
```
Remove genes with counts less than 10

```{r}
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
```

Run DESeq
```{r}
dds <- DESeq(dds)
```

```{r}
rawcounts.matrix <- counts(dds,normalized=F)
normalizedcounts.matrix <- counts(dds,normalized=T)
```

```{r}
vst_dds <- vst(dds)
dists <- dist(t(assay(vst_dds)))

```
```{r}
dists
```

TO DO: *****Label samples
```{r}

dendogram<-plot(hclust(dists),cex=0.6)

```
```{r}
labels(dendogram)
```

Trying to rename labels....
```{r}
dend <- dists %>%  hclust %>% as.dendrogram
dend %>% set("labels", c(rep('rtg4510',26),rep('J20',42),rep('rtg4510',4))) %>% set("labels_cex", 0.5) %>% plot
```
`

*** Add title, make graph cute (size bigger) Capitalize group
```{r}
plotPCA(vst_dds,intgroup=c("Model","Genotype","Age"))
```
# Obtain results based on different contrasts

### What genes are differentially expressed in tau pathology? mutant vs wt
```{r}
res_1 <- results(dds, contrast = c("Genotype","rtg4510","WT_TG"))
```
```{r}
res1_ordered <- res_1[order(res_1$pvalue),] 
head(res1_ordered,200)
```
Filter the res1_ordered byp value to obtain only those genes with p value less than 0.05
```{r}
#Filter Differentially Expressed Genes by p value 0.05
res1_ordered_filtered <- res1_ordered[res1_ordered$pvalue<0.05,]
```
See how many genes have p value equals to zero
```{r}
res1_ordered_filtered_2 <- res1_ordered[res1_ordered$pvalue==0,]
```

Genes with pvalue==0
```{r}
res1_ordered_filtered_2
```
Genes with pvalue<0.05
```{r}
res1_ordered_filtered
```


### -How does aging affect gene expression within each mouse model?
```{r}
#res_2 <- results(dds, contrast = c("Age","2","8"))
```
```{r}
?results
```

-How does the genotype within each mouse model affect gene expression at different time points?


#GO-Term Enrichment

Install Mouse annotation library:
```{r}
BiocManager::install("org.Mm.eg.db")
```

```{r}
install.packages("devtools")
devtools::install_github("stephenturner/annotables")
```
```{r}
install.packages("ggnewscale")
```

```{r}
library(biomaRt) #For conversion of transcript IDs to gene ID

library(annotables) #to retrieve grcm38 annotation for mouse genome
library(org.Mm.eg.db) #Mouse genome annotation
library(DOSE)
library(pathview)
library(clusterProfiler)
library(AnnotationHub) 
library(ensembldb)
library(tidyverse)
library(ggnewscale)
```


```{r}
grcm38
```
```{r}
res1_ordered
```

```{r}
idx <- grcm38$ensgene %in% rownames(res1_ordered)

head(idx)
```

```{r}
ids <- grcm38[idx, ]

head(ids)
```

```{r}
non_duplicates <- which(duplicated(ids$ensgene) == FALSE)
```

```{r}
ids <- ids[non_duplicates, ]

```


```{r}
res_tableOE_tb <- res1_ordered_filtered %>%
  data.frame() %>%
  rownames_to_column(var="gene") %>% 
  as_tibble()
```

```{r}
library("dplyr")
library("annotables")
res_ids = inner_join(res_tableOE_tb, ids, by=c("gene"="ensgene"))
```
```{r}
res_ids
```
```{r}

## Create background dataset for hypergeometric testing using all genes tested for significance in the results                 
allOE_genes <- as.character(res_ids$ensgene)


## Extract significant results
sigOE <- subset(res_ids, padj<0.05)

sigOE_genes <- as.character(sigOE$gene)
```


```{r}
## Run GO enrichment analysis 
ego <- enrichGO(gene = sigOE_genes, 
                universe =ids$ensgene ,
                keyType = "ENSEMBL",
                OrgDb = org.Mm.eg.db, 
                ont = "BP", 
                pAdjustMethod = "BH", 
                qvalueCutoff = 0.05, 
                readable = TRUE,
                pool=TRUE)
                
## Output results from GO analysis to a table
cluster_summary <- data.frame(ego)
```


```{r fig.width=15,fig.height=20}
dotplot(ego, showCategory=50)
```

```{r}

xyz = pairwise_
emapplot(xyz, showCategory = 50)
```

```{r}

```

Because we have transcript IDs, we want ensembl ids in order to be able to conduct GO Term enrichment we need ensembl ID


In order to conduct the hypergeometric test on each set of differentially expressed genes we need to have two sets of genes: a background set, and a significant differentially expressed gene set. The background set will be comprised of all differentially expressed genes and the genes of interest will be those with significant p values (0.05).

We are using 'ALL' for ontology because we want to see all of the differentially expressed genes accross all categories.
We are using the bonferroni correction.

```{r}
## Use mouse genome 
ego <- enrichGO(gene = rownames(res1_ordered_filtered), 
                universe =ids$ensgene ,
                keyType = "ENSEMBL",
                OrgDb = org.Mm.eg.db, 
                ont = "ALL", 
                pAdjustMethod = "BH", 
                qvalueCutoff = 0.05, 
                readable = TRUE,
                pool=TRUE)
                
## Output results from GO analysis to a table
enriched_genes_res1 <- data.frame(ego)
```
```{r}
enriched_genes_res1
```

```{r fig.width=15,fig.height=20}
dotplot(ego, showCategory=50)
```
```{r}

OE_foldchanges <- res1_ordered_filtered$log2FoldChange
OE_foldchanges<-head(OE_foldchanges,150)

names(OE_foldchanges) <- rownames(head(res1_ordered_filtered,150))

cnetplot(ego, 
         categorySize="pvalue", 
         showCategory = 5, 
         foldChange=OE_foldchanges, 
         vertex.label.font=2)
```


```{r}
emapplot(ego, showCategory = 50)
```





